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Psychiatric document retrieval using a discourse-aware model

Presenter : Wu, Jia-Hao

Authors : Liang-Chih Yu, Chung-Hsien Wu , Fong-Lin Jang

Artificial Intelligence (2009)

國立雲林科技大學National Yunlin University of Science and Technology

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2

Outline

Motivation

Objective

Methodology A Framework of psychiatric document retrieval

Discourse-aware retrieval model

Experiments

Conclusion

Personal Comments

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Motivation

Individuals in their daily life may suffer from negative or stressful life events.

Some website provide suggestions for individuals. Browsing and searching all consultation documents to identify the

relevant documents is time consuming and tends to become overwhelming.

MoneyJob

Argume

nt

death

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Objective

The paper proposes the use of high-level discourse-aware model. The model can extract from queries and documents to improve the

precision of retrieval results about the psychiatric document retrieval.

Some Retrieval models , such as vector space model and Okapi model , but there only consider the word-level information in queries and documents.

ConsultationDocuments

Query Recommendation

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Methodology

Events + Symptoms + RelationsDiscourse =Cause-effectTemporal-effect

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Methodology (Cont.)

Negative life event identification Find the patterns from the sentences.

Pattern induction Use the seed patterns from psychiatry web corpora using an

evolutionary inference algorithm.

SVM classification Use the SVM to train the patterns and transformed into its

corresponding feature vector.

<Husband, argue> →<Husband, fight> ,<husband, yell>,<wife, argue> , <husband, fight, money>

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Methodology (Cont.)

Symptom Identification Word segmentation and Part-Of-Speech (POS) tagging

Semantic dependency graph (SDG) construction.

Semantic label inference.

The identification of symptoms is sentence-based.

t = (modifier , head , relmodifier,head)

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Methodology (Cont.)

P((matters, worry about , goal) | <Anxiety>) is much higher than that in all the other label

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Methodology (Cont.)

Relation Identification Cause-effect relation

Temporal relation

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Methodology (Cont.)

Discourse-aware retrieval model

Similarity of events and symptoms

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Methodology (Cont.)

Similarity of relations

The relations are represented by symptom chians. Use the sequence kernel function to calculate the similarity of two

symptom chains.

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Methodology (Cont.)

Sequence kernel function Symptom 1 ： S1S2S3S4

Symptom 2 ： S3S2S1

Lengths 2 ： {S1S2,S1S3,S1S4,S2S3,S2S4,S3S4} & {S3S2,S3S1,S2S1}

Lengths 3 ： {S1S2S3,S1S2S4,S1S3S4,S2S3S4} & {S3S2S1}

1)()( 321321 3221 ssssss ssss

1321 )(

31 sssss

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Methodology (Cont.)

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Experiments

A total of 3650 consultation documents. 20 documents were randomly selected as the test query set.

100 documents were randomly selected as the tuning set.

The remaining 3530 documents were the reference set to be retrieved.

Use the discounted cumulative gain to evaluate the retrieval models. Level 0 : No discourse units are matched.

Level 1 : At least one discourse unit is partially matched.

Level 2 : All of the three discourse units are partially matched.

Level 3 : All of the three discourse units are partially matched, and at last one discourse unit is exactly matched.

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Experiments

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Conclusion

The discourse information can provide more precise information about users’ depressive problems.

The psychiatric document retrieval can support psychological treatments, so people can learn self-help skills to alleviate their symptoms.

The proposed framework can also be applied to other domains.

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Comments

Advantage The proposed content is easy to know, and the authors use

some instances to explain their ideas.

Drawback …

Application Psychological document retrieval.

Information Retrieval.